CN101510228B - Nonuniform simplifying method for STL model of products - Google Patents
Nonuniform simplifying method for STL model of products Download PDFInfo
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- CN101510228B CN101510228B CN2009100202074A CN200910020207A CN101510228B CN 101510228 B CN101510228 B CN 101510228B CN 2009100202074 A CN2009100202074 A CN 2009100202074A CN 200910020207 A CN200910020207 A CN 200910020207A CN 101510228 B CN101510228 B CN 101510228B
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Abstract
The invention provides an uneven simplifying method of a product STL model, which is characterized in that a topological adjacency relation of triangular patches is organized by adopting a R<*>S-tree dynamic spatial indexing structure, a clustering neighborhood of a sample patch is quickly obtained based on the structure, the curvature changes of a local molding surface of the product STL model are represented by an normal vector included angle between the sample patch and each triangular patch in the clustering neighborhood, self-adaptive clustering of the clustering neighborhood is implemented according to the curvature so as to obtain a triangular patch cluster, and the uneven simplification of the product STL model is realized based on the vertex average value and shape factor of eachtriangular patch cluster. The uneven simplifying method of the product STL model has strong data adaptability, effectively preserves the molding surface characteristics of the product STL model, and can effectively reduce data redundancy and remarkably raise the data preprocessing efficiency of reverse engineering.
Description
Technical field
The invention provides a kind of nonuniform simplifying method for STL model of products, belong to product reverse Engineering Technology field.
Background technology
In the product reverse-engineering, adopt equipment such as laser scanner to obtain the scattered point cloud data of product entity model surface usually, and these data are carried out triangulation generate the STL model, approach former solid model.Development along with scanning device, the STL model of products of expression product becomes increasingly complex, the quantity that comprises tri patch is very huge, such STL model is quite difficult when drafting, demonstration, editor, STL model of products is simplified, can effectively reduce its redundance, improve data pre-service efficient.
Existing STL model of products compressing method has two kinds: a kind of method is that space clustering is simplified method, adopt space lattice or Octree to divide STL model of products, by merging in the grid or the tri patch data in the Octree leaf node realize evenly simplifying of STL model of products.Zhou Kun etc. are at academic journal " robotization journal " 1999,25 (1), in the paper of delivering on the P1-8 " a kind of new Mesh simplification algorithm ", adopt Octree to divide STL model of products, realize evenly simplifying by the tri patch data that merge in the Octree leaf node based on the summit cluster.This method changes little STL model of products for surface-type feature, and it is comparatively accurate evenly to simplify the result, but exists STL model construction process complexity, the low deficiency that waits of adjacent dough sheet search efficiency; In addition, easily lose the surface-type feature of STL model during the STL model of products of this method surface treatment feature complexity.Another kind method is that topological cluster is simplified method, adopt the topological structure of adjacency list tissue products STL model, topology information according to tri patch is divided into some topological neighborhoods with STL model of products, simplifies the nonuniform simplifying of realizing STL model of products by the cluster of tri patch in the topological neighborhood.This method is better than space clustering to the nonuniform simplifying result of STL model of products and simplifies method, but because this method adopts the topological structure of static data structure tissue products STL model, STL model of products simplify the restriction that efficient is subjected to tri patch quantity; In addition, adjacency list process when the topological structure of tissue products STL model is loaded down with trivial details, has reduced the integral body of algorithm and has carried out efficient.
In sum, the structure surface model topological structure time is long when adopting existing STL model of products compressing method that the big STL model of tri patch data volume is simplified, the algorithm operational efficiency is low, loses surface-type feature when the STL model of products of surface-type feature complexity is simplified easily.
Summary of the invention
The object of the present invention is to provide a kind of nonuniform simplifying method for STL model of products that can overcome the local detail surface-type feature of above-mentioned defective, algorithm operational efficiency height, also effective retained product STL model.Its technical scheme is:
A kind of nonuniform simplifying method for STL model of products is characterized in that adopting following steps: 1) the R*-tree is improved to the R*S-tree, based on R
*The S-tree makes up the dynamic space index structure of STL model of products; 2) serve as the sampling dough sheet with arbitrary tri patch, adopt STL model of products dynamic space index structure range query algorithm queries and the contiguous tri patch of sampling dough sheet, with the contiguous tri patch that obtains topological neighborhood tri patch as the sampling dough sheet, the degree μ that departs from topological neighborhood tri patch geometric center according to the sampling dough sheet, topological neighborhood is divided into boundary neighborhood and inner neighborhood, boundary neighborhood and inner neighborhood handled obtain sampling dough sheet sub-clustering neighborhood, step is specially: 1. obtain sampling dough sheet topology neighborhood tri patch, it is deposited among the interim chained list knn, the geometric center m of calculating sampling dough sheet topology neighborhood tri patch, and be the radius of neighbourhood r of center of circle calculating sampling dough sheet topology neighborhood tri patch with m; 2. the calculating sampling dough sheet to topological neighborhood geometric center m apart from d, according to formula
The calculating sampling dough sheet departs from the degree μ of neighborhood geometric center; 3. obtain sampling dough sheet irrelevance threshold value, if μ less than the irrelevance threshold value, will gather knn as a sub-clustering neighborhood, at R
*Tri patch in the S-tree in the deletion knn; 4. empty knn, if algorithm finishes, execution in step 5., otherwise, inquire about the topological neighborhood tri patch of a down-sampling dough sheet, execution in step 1.; 5. inquire about tri patch free in the STL model of products, it is added in the nearest sub-clustering neighborhood; 3) normal vector of the normal vector of calculating sampling dough sheet and sub-clustering neighborhood tri patch thereof, characterize the curved transition situation of the local profile of STL model of products with the angle of sampling dough sheet and its sub-clustering neighborhood tri patch normal vector, search the sub-clustering neighborhood of all tri patchs in the STL model of products, with the sampling dough sheet is the center, self-adaptation expansion inquiry neighborhood tri patch, and calculate it and the normal vector angle of the dough sheet of sampling, sampling dough sheet and its sub-clustering neighborhood tri patch normal vector angle are divided into some triangular facets bunch greater than the sub-clustering neighborhood of angle threshold value, with the tri patch that is positioned at geometric center in the sub-clustering Vee formation face bunch is the sampling dough sheet, obtain the normal vector angle of other tri patch in itself and the triangular facet bunch, the iteration sub-clustering; 4) triangular facet in the STL model of products dynamic space index structure bunch is handled, realize the nonuniform simplifying of STL model of products, step is specially: the number of at first obtaining each triangular facet bunch intermediate cam dough sheet, calculate successively each triangular facet bunch intermediate cam dough sheet three summits coordinate and, with the coordinate on three summits with divided by the number of triangular facet bunch intermediate cam dough sheet, obtaining the apex coordinate average of triangular facet bunch, is that the summit is according to counterclockwise sequential build tri patch with the apex coordinate average of triangular facet bunch; Calculate each area and girth then according to the tri patch of apex coordinate average structure, and be references object with the equilateral triangle of unit area, obtain the form factor of this tri patch with respect to equilateral triangle, inquire about the magnitude relationship of the tri patch shape threshold value of the form factor of this tri patch and appointment, if the form factor of this tri patch is greater than the tri patch shape threshold value of appointment, then the mid point of this tri patch longest edge is linked to each other with the summit that is not total to the limit, obtain two new tri patchs, calculate the form factor of these two tri patchs respectively, inquire about itself and the magnitude relationship of specifying tri patch shape threshold value, continue to add some points to adjust the tri patch of form factor greater than the tri patch shape threshold value of appointment; At last all triangular facets bunch tri patch that processing generates is added in the STL model of products dynamic space index structure of new establishment, realize the nonuniform simplifying of STL model of products.
For realizing goal of the invention, described nonuniform simplifying method for STL model of products in step 1), improves R
*-tree dynamic space index data structure obtains R
*The method of S-tree is specifically: STL model of products tri patch data are read in the storer, and set up the linear linked list storage organization for the tri patch data in the STL model of products, with tri patch and index node MBR is that minimum area-encasing rectangle unification is expressed as four-dimensional some object (x, y, z, r), x wherein, y, z is the MBR centre coordinate, and r is a MBR circumsphere radius value, proposes new node and optimizes criterion and bunch collection appointment criterion, adopt the realization cluster sub-clustering of k-means algorithm and carry out the node division, make up triangle grid model dynamic space index structure.
Be to realize goal of the invention, described nonuniform simplifying method for STL model of products in step 1), as node optimality judging quota, proposes R with the circumsphere parameter of MBR
*The node of S-tree is optimized criterion: the circumsphere volume of (1) node MBR minimizes; (2) the circumsphere degree of overlapping of node MBR minimizes, and establishes the circumsphere S of two MBR
1, S
2Radius be respectively r
1, r
2, two centre ofs sphere distance is e, with
Weigh the overlapping degree of MBR, the δ value is bigger, and the overlapping degree of MBR is just bigger, and the δ value is littler, and MBR is just bigger from degree.
For realizing goal of the invention, described nonuniform simplifying method for STL model of products in step 1), is taken all factors into consideration distance and circumsphere radius between each node center, proposes a new bunch collection and assigns rule: establish b
iBe node to be assigned, by formula
Calculate the approximate gravitation between itself and each initial clustering core, wherein r
1And r
2Circumsphere radius, the d that is respectively two node MBR is the distance between the two node MBR circumsphere centre ofs sphere, and the cluster core that produces maximum gravitation is b
iSo the core that is depended on is with node b
iBe assigned to bunch concentrating of this cluster core place.
For realizing goal of the invention, described nonuniform simplifying method for STL model of products, in step 1), the step that adopts the k-means algorithm to realize the cluster sub-clustering and carry out the node division is specifically: with the center of index node centre distance a pair of node MBR farthest as the initial clustering core, each non-cluster nexus index node is added to apart from nearest bunch the concentrating of cluster core, upgrade the cluster core of each bunch collection, and compare with original cluster core, if the cluster core is identical or the sub-clustering number of times then finishes sub-clustering above maximum sub-clustering number of times, otherwise continue sub-clustering.
The present invention compared with prior art has the following advantages:
1) adopts R
*The S-tree is set up the dynamic space index structure of STL model of products, divide the topological relation of organizing tri patch by space clustering, improved the adaptability of nonuniform simplifying method for STL model of products for various complex product STL models to STL model of products intermediate cam dough sheet;
2) adopt R
*S-tree is set up the dynamic space index structure of STL model of products, can obtain sampling dough sheet sub-clustering neighborhood fast based on the range query algorithm of this structure, has effectively improved the efficient of STL model of products nonuniform simplifying;
3) carry out nonuniform simplifying according to the curved transition of STL model of products, improved adaptability, effectively kept the local detail surface-type feature of STL model of products for all kinds of complex product STL models.
Description of drawings
Fig. 1 is a program flow diagram of the present invention;
Fig. 2 is the STL model of products dynamic space index structure one-piece construction synoptic diagram that the present invention sets up;
Fig. 3 is dynamic space index structure index node standardization expression of the present invention;
Fig. 4 is k-means algorithm sub-clustering realization flow figure of the present invention;
Fig. 5～Fig. 9 is each layer of dynamic space index structure node MBR illustraton of model that the present invention is set up Venus head portrait model;
Figure 10 is a boundary neighborhood synoptic diagram of the present invention;
Figure 11 is the inner neighborhood synoptic diagram of the present invention;
To be the present invention carry out self-adaptation sub-clustering program flow diagram according to the normal vector angle of sampling dough sheet and its sub-clustering neighborhood tri patch to the sub-clustering neighborhood to Figure 12;
Figure 13 is sampling dough sheet and other tri patch methods arrow angle synoptic diagram in the STL model of products sub-clustering neighborhood of the present invention;
Figure 14 is a STL model of products sub-clustering neighborhood self-adaptation sub-clustering result schematic diagram of the present invention;
Figure 15 is STL model of products nonuniform simplifying process synoptic diagram figure of the present invention;
To be the present invention carry out design sketch behind the nonuniform simplifying to embodiment one Venus head portrait STL model to Figure 16;
Figure 17 is a Micky Mouse STL model in the embodiment of the invention two;
To be the present invention carry out design sketch behind the nonuniform simplifying to embodiment two Micky Mouse STL models to Figure 18.
Embodiment
The invention will be further described below in conjunction with accompanying drawing:
Fig. 1 is the program realization flow of STL model of products nonuniform simplifying of the present invention.STL model of products data entry program 1 is responsible for reading in the STL model of products data file, and sets up the linear linked list storage organization for the tri patch data in the STL model of products, travels through to support the triangle grid data linear precedence.STL model of products dynamic space index structure construction procedures 2 adopts nested MBR that the tri patch data are carried out the dynamic space cluster and divides, for the data linear linked list that data entry program 1 is generated is set up upper strata R*S-tree dynamic space index structure.Obtaining sampling dough sheet sub-clustering neighborhood program 3 adopts the contiguous tri patch of STL model of products dynamic space index structure range query algorithm queries sampling dough sheet to obtain topological neighborhood tri patch, the degree that departs from topological neighborhood tri patch geometric center according to the sampling dough sheet is divided into boundary neighborhood and inner neighborhood with topological neighborhood, boundary neighborhood and inner neighborhood is handled obtained sampling dough sheet sub-clustering neighborhood.Sub-clustering neighborhood self-adaptation sub-clustering program 4 adopts tri patch self-adaptations expansion algorithms that tri patch is carried out the self-adaptation sub-clustering, for the nonuniform simplifying of STL model of products provides triangular facet bunch set.STL model of products nonuniform simplifying program 5 realizes the nonuniform simplifying of STL model of products.
The STL model of products dynamic space index structure one-piece construction synoptic diagram that Fig. 2 sets up for STL model of products dynamic space index structure construction procedures 2 of the present invention.The data structure of STL model of products dynamic space index structure is divided into index level and data Layer, and index level is made of inner node, leaf node and data node; Data Layer is a data link table, and its node has the ability of visit higher level index level.The index level node is divided into index node and data node, and the data node has only the pointer that points to concrete spatial data object.Type sign in the index node structure is used to judge that this node is inner node or leaf node, and it is inner node that type equals 0 this node of expression, and it is leaf node that type equals 1 this node of expression.The child node of inner node remains index node, and the child node of leaf node is the data node, can point to concrete data object by the data node.As shown in Figure 3 with STL model of products intermediate cam dough sheet and index node MBR unified be expressed as four-dimensional some object (x, y, z, r), x wherein, y, z are the MBR centre coordinate, r is a MBR circumsphere radius value.For the upper limit M and the lower limit m of the child node number of each layer of STL model of products dynamic space index structure node, and node inserts the value of number R again, is provided with voluntarily according to the scale of triangle grid data by the user, gets m=M * 40% usually, and
R＝M×30％。Adopt realization flow that the k-means algorithm carries out the sub-clustering of tri patch ensemble space cluster as shown in Figure 4: with the center of index node centre distance a pair of node MBR farthest as the initial clustering core, adding apart from the sub-clustering center data object to nearest bunch concentrates, upgrade each bunch collection core, and compare with an original bunch of collection core, if bunch collection core is identical or the sub-clustering number of times then finishes sub-clustering above maximum sub-clustering number of times, otherwise continues sub-clustering.
Fig. 5～Fig. 9 calls each layer of dynamic space index structure node MBR illustraton of model that 2 pairs of Venus head portraits of STL model of products dynamic space index structure construction procedures STL model is set up for the present invention.Testing used triangle grid data quantity is 39649, and the indexing parameter m=8, the M=20 that are adopted insert nodal point number R=6 again, and the Venus head portrait STL model dynamic space index structure data structure structure time is about 0.287224 second.Wherein Fig. 5 has shown Venus head portrait STL model, and Fig. 6 has shown dynamic space index structure root node MBR, and Fig. 7 has shown second layer node MBR, and Fig. 8 has shown leaf node MBR, and Fig. 9 has shown data node MBR.This experiment shows, adopts R*S-tree dynamic space index structure can accurately realize the space clustering division of STL model of products.
Figure 10～11 obtain boundary neighborhood for the present invention calls and inner neighborhood program 3 serves as the sampling dough sheet with current tri patch, adopt its contiguous tri patch of STL model of products dynamic space index structure range query algorithm queries to obtain topological neighborhood tri patch, the degree that departs from topological neighborhood geometric center according to the sampling dough sheet is obtained the synoptic diagram of boundary neighborhood and inner neighborhood.The contiguous tri patch of the inquiry that test is adopted is counted k=10, sampling dough sheet irrelevance threshold value μ=0.5.Wherein Figure 10 has shown boundary neighborhood, and Figure 11 has shown inner neighborhood.
Figure 12 carries out self-adaptation sub-clustering program flow diagram according to the normal vector angle of sampling dough sheet and its sub-clustering neighborhood tri patch to the sub-clustering neighborhood for the present invention calls sub-clustering neighborhood self-adaptation sub-clustering program 4.Calculate the normal vector of sampling dough sheet in the sub-clustering neighborhood, and the normal vector angle of sampling dough sheet and its place sub-clustering neighborhood tri patch, employing angle threshold value ζ=15 °, with the sampling dough sheet is the center, the neighborhood tri patch of self-adaptation expansion inquiry sampling dough sheet, calculate its method and vow angle with the sampling dough sheet, angle is carried out the self-adaptation sub-clustering greater than the tri patch of angle threshold value ζ, with the tri patch that is positioned at geometric center in the sub-clustering Vee formation face bunch is the sampling dough sheet, obtain the method for other tri patch in this sampling dough sheet and the triangular facet bunch and vow angle, iteration self-adapting sub-clustering.
Figure 13 vows the angle synoptic diagram for sampling dough sheet and its sub-clustering neighborhood tri patch method in the STL model of products sub-clustering neighborhood of the present invention.Rectangular area a, b, c are triangle mesh curved surface sub-clustering neighborhood, and regional a profile is comparatively smooth, and curved transition is little, and sampling dough sheet and its sub-clustering neighborhood tri patch method are vowed angle α＜ζ; Zone b surface-type feature is comparatively complicated, and curved transition is bigger, and sampling dough sheet and its left side sub-clustering neighborhood tri patch method are vowed angle β＞ζ; Zone c surface-type feature complexity, curved transition is big, and sampling dough sheet and its both sides sub-clustering neighborhood tri patch method are vowed angle γ＞β＞ζ.The angle of vowing according to sub-clustering neighborhood tri patch method is determined the local profile feature of triangle mesh curved surface, and the sub-clustering neighborhood of surface-type feature complexity is divided into some bunches, keeps the surface-type feature of triangle mesh curved surface.Be rectangular area a as shown in figure 14, b, the self-adaptation sub-clustering effect synoptic diagram of c, zone a is divided into cluster, zone b is divided into two bunches, and regional c is divided into three bunches, by the self-adaptation sub-clustering of triangular facet bunch, the regional triangular facet number of clusters amount that the triangle mesh curved surface curved transition is little is few, and the regional triangular facet number of clusters amount that curved transition is big is many.
As shown in figure 15, be STL model of products nonuniform simplifying program 5 algorithm flow charts of the present invention.Concrete steps are: 1. search the triangular facet bunch in the Venus head portrait STL model dynamic space index structure, the number of establishing triangular facet bunch intermediate cam dough sheet is n, and the coordinate on first summit of tri patch is V
1(x
1i, y
1i, z
1i), the coordinate on second summit is V
2(x
2i, y
2i, z
2i), the coordinate on the 3rd summit is V
3(x
3i, y
3i, z
3i), wherein (1≤i≤n), according to formula
Calculate the summit average V of triangular facet bunch intermediate cam dough sheet
1, V
2, V
3Judging point V
1, V
2, V
3Can constitute triangle according to counterclockwise order, if can not constitute triangle, 3. execution in step if can constitute triangle, makes up with a V
1, V
2, V
3Tri patch t for the summit; 2. unit of account area equilateral triangle girth is 4.5590141139mm, and with it as proportionality constant K, the area of establishing t is s, girth is p, according to formula
Calculate the form factor q of t; The shape threshold value Q that compares q and appointment, if q is greater than Q, the employing method of adding some points is adjusted tri patch t, concrete strategy is that the mid point of t longest edge is linked to each other with the summit that is not total to the limit, obtain two new tri patchs, continuing to add some points to adjust newly obtains the tri patch of tri patch intermediate cam dough sheet form factor greater than Q; 3. search the next triangular facet bunch in the Venus head portrait STL model dynamic space index structure.At last all triangular facets bunch tri patch that processing generates is added in the STL model of products dynamic space index structure of new establishment, realize the nonuniform simplifying of STL model of products.By simplifying of triangular facet bunch, the tri patch quantity that the smooth zone of triangle mesh curved surface profile keeps is few, the tri patch quantity that the zone of profile complexity keeps is many, on the basis of simplifying the bulk redundancy tri patch, has kept the surface-type feature of triangle mesh curved surface effectively.
As shown in figure 16, being the present invention carries out design sketch behind the nonuniform simplifying to embodiment one Venus head portrait STL model.
As shown in figure 17, be embodiment two Micky Mouse STL models, to be the present invention carry out design sketch behind the nonuniform simplifying to embodiment two Micky Mouse STL models to Figure 18.
The nonuniform simplifying method of other complex product STL model is the same.
Claims (5)
1. a nonuniform simplifying method for STL model of products is characterized in that adopting following steps: 1) the R*-tree is improved to the R*S-tree, makes up the dynamic space index structure of STL model of products based on the R*S-tree; 2) serve as the sampling dough sheet with arbitrary tri patch, adopt STL model of products dynamic space index structure range query algorithm queries and the contiguous tri patch of sampling dough sheet, with the contiguous tri patch that obtains topological neighborhood tri patch as the sampling dough sheet, the degree μ that departs from topological neighborhood tri patch geometric center according to the sampling dough sheet, topological neighborhood is divided into boundary neighborhood and inner neighborhood, boundary neighborhood and inner neighborhood handled obtain sampling dough sheet sub-clustering neighborhood, step is specially: 1. obtain sampling dough sheet topology neighborhood tri patch, it is deposited among the interim chained list knn, the geometric center m of calculating sampling dough sheet topology neighborhood tri patch, and be the radius of neighbourhood r of center of circle calculating sampling dough sheet topology neighborhood tri patch with m; 2. the calculating sampling dough sheet to topological neighborhood geometric center m apart from d, according to formula
The calculating sampling dough sheet departs from the degree μ of neighborhood geometric center; 3. obtain sampling dough sheet irrelevance threshold value, if μ less than the irrelevance threshold value, will gather knn as a sub-clustering neighborhood, the tri patch in the R*S-tree in the deletion knn; 4. empty knn, if algorithm finishes, execution in step 5., otherwise, inquire about the topological neighborhood tri patch of a down-sampling dough sheet, execution in step 1.; 5. inquire about tri patch free in the STL model of products, it is added in the nearest sub-clustering neighborhood; 3) normal vector of the normal vector of calculating sampling dough sheet and sub-clustering neighborhood tri patch thereof, characterize the curved transition situation of the local profile of STL model of products with the angle of sampling dough sheet and its sub-clustering neighborhood tri patch normal vector, search the sub-clustering neighborhood of all tri patchs in the STL model of products, with the sampling dough sheet is the center, self-adaptation expansion inquiry neighborhood tri patch, and calculate it and the normal vector angle of the dough sheet of sampling, sampling dough sheet and its sub-clustering neighborhood tri patch normal vector angle are divided into some triangular facets bunch greater than the sub-clustering neighborhood of angle threshold value, with the tri patch that is positioned at geometric center in the sub-clustering Vee formation face bunch is the sampling dough sheet, obtain the normal vector angle of other tri patch in itself and the triangular facet bunch, the iteration sub-clustering; 4) triangular facet in the STL model of products dynamic space index structure bunch is handled, realize the nonuniform simplifying of STL model of products, step is specially: the number of at first obtaining each triangular facet bunch intermediate cam dough sheet, calculate successively each triangular facet bunch intermediate cam dough sheet three summits coordinate and, with the coordinate on three summits with divided by the number of triangular facet bunch intermediate cam dough sheet, obtaining the apex coordinate average of triangular facet bunch, is that the summit is according to counterclockwise sequential build tri patch with the apex coordinate average of triangular facet bunch; Calculate each area and girth then according to the tri patch of apex coordinate average structure, and be references object with the equilateral triangle of unit area, obtain the form factor of this tri patch with respect to equilateral triangle, inquire about the magnitude relationship of the tri patch shape threshold value of the form factor of this tri patch and appointment, if the form factor of this tri patch is greater than the tri patch shape threshold value of appointment, then the mid point of this tri patch longest edge is linked to each other with the summit that is not total to the limit, obtain two new tri patchs, calculate the form factor of these two tri patchs respectively, inquire about itself and the magnitude relationship of specifying tri patch shape threshold value, continue to add some points to adjust the tri patch of form factor greater than the tri patch shape threshold value of appointment; At last all triangular facets bunch tri patch that processing generates is added in the STL model of products dynamic space index structure of new establishment, realize the nonuniform simplifying of STL model of products.
2. nonuniform simplifying method for STL model of products as claimed in claim 1, it is characterized in that: in step 1), improve method that R*-tree dynamic space index data structure obtains R*S-tree specifically: STL model of products tri patch data are read in the storer, and set up the linear linked list storage organization for the tri patch data in the STL model of products, with tri patch and index node MBR is that minimum area-encasing rectangle unification is expressed as four-dimensional some object (x, y, z, r), x wherein, y, z is the MBR centre coordinate, and r is a MBR circumsphere radius value, proposes new node and optimizes criterion and bunch collection appointment criterion, adopt the realization cluster sub-clustering of k-means algorithm and carry out the node division, make up triangle grid model dynamic space index structure.
3. nonuniform simplifying method for STL model of products as claimed in claim 2, it is characterized in that: in step 1), as node optimality judging quota, the node that proposes the R*S-tree is optimized criterion: the circumsphere volume of (1) node MBR minimizes with the circumsphere parameter of MBR; (2) the circumsphere degree of overlapping of node MBR minimizes, and establishes the circumsphere S of two MBR
1, S
2Radius be respectively r
1, r
2, two centre ofs sphere distance is e, with
Weigh the overlapping degree of MBR, the δ value is bigger, and the overlapping degree of MBR is just bigger, and the δ value is littler, and MBR is just bigger from degree.
4. nonuniform simplifying method for STL model of products as claimed in claim 2 is characterized in that: in step 1), take all factors into consideration distance and circumsphere radius between each node center, propose a new bunch collection and assign rule: establish b
iBe node to be assigned, by formula
Calculate the approximate gravitation between itself and each initial clustering core, wherein r
1And r
2Circumsphere radius, the d that is respectively two node MBR is the distance between the two node MBR circumsphere centre ofs sphere, and the cluster core that produces maximum gravitation is b
iSo the core that is depended on is with node b
iBe assigned to bunch concentrating of this cluster core place.
5. nonuniform simplifying method for STL model of products as claimed in claim 2, it is characterized in that: in step 1), the step that adopts the k-means algorithm to realize the cluster sub-clustering and carry out the node division is specifically: with the center of index node centre distance a pair of node MBR farthest as the initial clustering core, each non-cluster nexus index node is added to apart from nearest bunch the concentrating of cluster core, upgrade the cluster core of each bunch collection, and compare with original cluster core, if the cluster core is identical or the sub-clustering number of times then finishes sub-clustering above maximum sub-clustering number of times, otherwise continue sub-clustering.
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| CN114494651B (en) * | 2022-04-18 | 2022-09-02 | 龙芯中科(合肥)技术有限公司 | Tessellation processing method, tessellation processing device, electronic device and storage medium |
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